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  "cells": [
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      },
      "execution_count": 6,
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (4.25.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.11.1)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.13.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.4.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.9.24)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (3.0.4)\n"
          ]
        }
      ]
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        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"roberta-base\")"
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "import random\n",
        "\n",
        "# Load the RoBERTa model and tokenizer\n",
        "model = transformers.RobertaModel.from_pretrained('roberta-base')\n",
        "tokenizer = transformers.RobertaTokenizer.from_pretrained('roberta-base')\n",
        "\n",
        "def generate_response(question):\n",
        "  # Encode the input text using the tokenizer\n",
        "  input_ids = tokenizer.encode(question, return_tensors='pt').to('cpu')\n",
        "\n",
        "  # Generate the final hidden state of the model\n",
        "  hidden_state = model(input_ids).last_hidden_state\n",
        "\n",
        "  # Calculate the logits for each response\n",
        "  logits = hidden_state[:, 0, :]\n",
        "\n",
        "  # Choose the response with the highest probability\n",
        "  response_index = logits.argmax().item()\n",
        "  responses = [\"Yes\", \"No\", \"I'm not sure\"]\n",
        "\n",
        "  # Randomly select a response if there is a tie\n",
        "  if logits[0][response_index] == logits.max().item():\n",
        "    response_index = random.randint(0, 2)\n",
        "\n",
        "  return responses[response_index]\n",
        "\n",
        "print(generate_response(\"Are dogs pets?\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bL_BlaJoFgJU",
        "outputId": "f99a510a-3d0c-4633-cc9b-cebe43adda9b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: ['lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.decoder.weight', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I'm not sure\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfDFgi0aIJ31",
        "outputId": "3e505b8c-7bcf-4aa6-ef14-c3a1ed934908"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully installed anyio-3.6.2 fastapi-0.88.0 ffmpy-0.3.0 gradio-3.14.0 h11-0.14.0 httpcore-0.16.2 httpx-0.23.1 linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.3 mdurl-0.1.2 orjson-3.8.3 pycryptodome-3.16.0 pydub-0.25.1 python-multipart-0.0.5 rfc3986-1.5.0 sniffio-1.3.0 starlette-0.22.0 uc-micro-py-1.0.1 uvicorn-0.20.0 websockets-10.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import transformers\n",
        "import random\n"
      ],
      "metadata": {
        "id": "OdbiSdpIIL6f"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = gr.inputs.Textbox(placeholder=\"Ask a yes or no question\")\n",
        "outputs = gr.outputs.Textbox()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJwEc2HtIPtW",
        "outputId": "1902c65c-d0e4-4196-f498-0670f2cdf7f3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/gradio/inputs.py:26: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/outputs.py:21: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def magic_8_ball(question):\n",
        "  # Load the RoBERTa model and tokenizer\n",
        "  model = transformers.RobertaModel.from_pretrained('roberta-base')\n",
        "  tokenizer = transformers.RobertaTokenizer.from_pretrained('roberta-base')\n",
        "\n",
        "  # Encode the input text using the tokenizer\n",
        "  input_ids = tokenizer.encode(question, return_tensors='pt').to('cpu')\n",
        "\n",
        "  # Generate the final hidden state of the model\n",
        "  hidden_state = model(input_ids).last_hidden_state\n",
        "\n",
        "  # Calculate the logits for each response\n",
        "  logits = hidden_state[:, 0, :]\n",
        "\n",
        "  # Choose the response with the highest probability\n",
        "  response_index = logits.argmax().item()\n",
        "  responses = [\"Yes\", \"No\", \"I'm not sure\"]\n",
        "\n",
        "  # Randomly select a response if there is a tie\n",
        "  if logits[0][response_index] == logits.max().item():\n",
        "    response_index = random.randint(0, 2)\n",
        "\n",
        "  return responses[response_index]\n"
      ],
      "metadata": {
        "id": "ezYe80R4ITCY"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = gr.Interface(magic_8_ball, inputs, outputs)\n",
        "\n",
        "app.launch(share=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "toQhC4-YIY8v",
        "outputId": "a2a50d7c-0274-492c-b650-a2816b504af0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n",
            "\n",
            "Setting up a public link... we have recently upgraded the way public links are generated. If you encounter any problems, please report the issue and downgrade to gradio version 3.13.0\n",
            ".\n",
            "Running on public URL: https://bbc6682d-a129-4b89.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://bbc6682d-a129-4b89.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
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          "metadata": {},
          "execution_count": 20
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    }
  ]
}